基于组织性能预测的柔性化轧制工艺制定方法
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摘要
随着科学技术和经济的发展,钢铁产品的大规模需求与生产技术和组织管理之间产生了矛盾,有必要提供一种能够导致轧制过程具有较大灵活性和适应性的轧制新技术来缓和这个矛盾,这种新技术被称为柔性轧制技术。换句话说,柔性化轧制技术就是通过改变轧制工艺,使用同一种化学成分的原料轧制出不同性能级别的产品,或者使用不同化学成分的原料轧制出相同性能级别的产品。超级钢和复相钢以及相应设备的成功开发为柔性化轧制技术的实现提供了实践依据。同时,精确预测热轧产品组织性能为柔性化轧制技术的实现提供了理论基础。本文搜集了大量的实际生产数据,并系统分析了各因素对热轧板材组织性能的影响,建立了板材力学性能与其影响因素之间的定量关系；另外,建立了热轧过程的温度场模型、再结晶模型和相变模型,精确描述出板材在整个热轧过程的微观组织演变,挖掘出力学性能变化的根源和机理。根据基于人工神经网络的组织性能预测技术和基于物理冶金模型的组织性能预测技术,从宏观和微观角度精确预测板材的力学性能。在高精度预测板材力学性能的基础上,逆向计算了热轧温度制度,实现通过改变热轧温度制度,获得要求强度的产品；另外,分析了热轧过程各参数对微观组织演变的影响,根据力学性能变化的机理,合理调整工艺制度,实现板材产品的柔性化轧制。根据基于组织性能预测的柔性化轧制制定方法,编制了相应的软件。本文的创新点和主要研究内容如下：
(1)从宏观角度定量分析了各因素与热轧板材组织性能的关系以及建立了它们之间的函数关系。以均化处理方法分析各因素对热轧板材力学性能的影响,并建立了普碳钢和微合金钢力学性能的回归模型和人工神经网络模型。结果表明,回归模型和人工神经网络模型均可以精确预测热轧产品的力学性能,且后者比前者精度高,回归模型可以为化学成分设计提供理论指导。同时,通过人工神经网络模型预测了各因素对热轧板材力学性能的影响,预测结果与均化处理结果基本一致。
(2)提出提高神经网络模型预测能力的方法以及在高精度人工神经网络模型的基础上逆向计算热轧温度制度。隐藏层单元数和训练时间等参数是影响人工神经网络模型预测能力的重要因素,提出通过遗传算法来优化神经网络结构的方法,提高了神经网络模型的预测能力。在优化后的高精度神经网络模型的基础上,通过遗传算法计算温度制度,包括第一阶段开轧温度、第二阶段开轧温度(存在于控制轧制过程中)、终轧温度和终冷温度(存在于控制冷却过程中),计算的温度制度通过回归模型计算得到的力学性能与预期的力学性能非常吻合。因此,从理论上获得了柔性化轧制技术的定量计算方法,实现了通过相同化学成分的钢坯生产不同力学性能的产品,以及通过不同化学成分的钢坯生产相同力学性能的产品。
(3)建立了输送辊道对轧件温度场定量影响模型以及提出了提高温度场模拟精度的方法。建立了输送辊与轧件之间接触的热传导模型,考虑了轧件与输送辊之间的传热。结果表明,输送辊对轧件温度场的影响相对来说比较小,大约能使轧件整体温度下降14～18℃,轧件上表面比下表面高约4-16℃,本模型有利于进一步提高热轧温度场模拟精度。通过Monte Carlo算法小幅度优化了热轧过程温度场。结果表明,优化后的温度场与实测温度更接近,这种方法也有利于提高温度场的计算精度,同时可以实现温度场的在线精确模拟。
(4)建立了管线钢X65再结晶过程微观组织演变模型和平均流变应力模型,提出了晶粒细化工艺。对管线钢再结晶过程进行模拟,分析了垂直于轧制方向的二维断面中各位置奥氏体晶粒尺寸和再结晶分数的变化过程,提出了有利于铁素体晶粒细化的工艺,并模拟了相应微观组织演变过程。结果表明,通过加大精轧入口前两个道次的压下量或者降低精轧入口温度和精轧出口温度,可以细化相变前奥氏体晶粒尺寸,有利于随后冷却过程的铁素体晶粒细化。如果精轧入口温度过低,不利于相变前奥氏体晶粒的细化和生产设备的保护,不过出现的残余应变能提供大量的铁素体形核位置,仍能获得细小的铁素体晶粒。建立了平均流变应力的回归模型和人工神经网络模型,比传统的Misaka模型更精确。
(5)建立了考虑溶质拖曳、残余应变、奥氏体晶粒等因素影响的铁素体相变模型。通过热膨胀实验获得热膨胀曲线,结合金相法确定铁素体相变随温度和时间变化的过程,在铁素体形核长大模型的基础上,计算了Nb含量与铁素体相变开始温度的定量关系。结果表明,铁素体相变开始发生时铁素体晶核周围的临界碳浓度与温度和冷却速率无关,随着钢中Nb含量的增加而线性增大；当Nb含量较低时,铁素体形核结束温度随着Nb含量的增加而降低,Nb含量大于0.023mass%后,形核温度并不会随着Nb含量的增加而降低；在奥氏体中固溶的Nb会抑制铁素体实际相变的发生；小冷却速率(<5℃·s-1)的条件下,随着冷却速率的增大,铁素体相变开始温度急剧降低,当冷却速率大于5℃·s-1时,增大的冷却速率与降低的铁素体相变开始温度近似成正比；通过模型计算的铁素体实际相变温度与实测的铁素体实际相变温度吻合；当Nb含量超过一定程度,随着微量合金元素Nb的增加,可能在热轧过程中导致残余应变的增加,以及含Nb析出物的增加,它们均能促进铁素体形核,提高铁素体形核速率,从而可能提高铁素体的转变体积分数,但是这种铁素体分数的增量很小。
(6)基于物理冶金模型的组织性能预测在板材生产过程中的应用：根据现场的所有过程数据,模拟了板材生产的温度场、再结晶过程和相变过程的演变。结果表明,预测的最终微观组织与金相组织吻合较好,微合金钢热轧微观组织演变模型具有较高预测精度；当第二阶段轧制过程位于未再结晶区时,适当增加该过程的总压下量,有利于提高随后相变过程的形核率。当铁素体后期长大阶段的冷却速率较小时,提高铁素体形核阶段和前期长大阶段的冷却速率,有利于提高总形核率,细化铁素体晶粒尺寸,提高板材的力学性能；根据热轧板材模拟结果,可以适当调整生产工艺制度,获得理想的力学性能,实现板材的柔性化轧制。
(7)开发的基于组织性能预测的柔性化轧制工艺制定软件主要功能是预测板材生产过程的微观组织演变,在线预测板材力学性能以及逆向计算温度制度。
There was a contradiction occurring with development of science, technology and economy between mass demand of iron & steel product and manufacturing technique & systemic supervisor. Therefore, it is necessary to offer a new rolling technique with larger flexibility and compatibility in rolling process, called flexible rolling technology. In other words, flexible rolling technology involves that products with distinct properties level are manufactured with raw materials with identical chemical compositions, or that products with identical properties level are manufactured with raw materials with distinct chemical compositions. Successful development of super steels, multiphase steels and corresponding devices is the practical base for the realization of flexible rolling technology, and additionally, precise prediction of microstructure and properties of hot rolled production is the academic base for that. Mass data in practical hot rolling production were collected, and relationship between each factor and microstructure & properties was systemically analyzed. In addition, Temperature models, recrystallization models and phase transformation models in hot rolling was established to exactly describe microstructure evolution of the whole hot rolling process and the foundation and mechanism of mechanical properties changing was explored. From macroscopic and microcosmic point of view, the final mechanical properties of hot rolled product were predicted by prediction technology of microstructure & properties based on Artificial Neural Network (ANN) or physic-metallurgy models. On the basis of high-precision predicting ability, temperature schedule was constituted reversely, and relationship between each factor and microstructure & properties was analyzed, as well as that production of desire strength obtained by changing temperature schedule of hot rolling was realized. In addition, effect of hot rolling parameters on corresponding microstructure evolution was analyzed, and processing system was adjusted according to mechanism of mechanical properties changing so as to realize flexible rolling of steels. According to methodology of establishing flexible technology based on prediction of microstructure and properties, corresponding software was programmed. The innovation and main research content of this paper is as follows:
(1) From macroscopic point of view, the function relationship between each factor and microstructure & properties was analyzed and created. The effect of each factor on mechanical properties was analyzed by meanly handling method, and regressed models and ANN models for mechanical properties of plain carbon steels and microalloyed steels were created. It is shown that both regressed models and ANN models can be used to precisely predict the mechanical properties of hot rolled product, and regressed models was taken as an academic guide for chemical compositions design, whereas the later model is more precise than the former one. Predicted result for the influence of each factor on the mechanical properties by ANN models is in good agreement with calculated value by meanly handling method.
(2) Method for improving predicting ability of ANN models and constituting temperature schedule reversely based on high-precision ANN models was put forward. Considering that parameters such as neurons of hidden layer and training time, etc are important factors for ANN models, an optimizing ANN architecture method by Genetic Algorithm (GA) was put forward, so that predicting ability of ANN models was improved. Based on the optimized high-precision ANN models, temperature schedule including 1st starting temperature,2nd starting temperature, which exists in the controlled rolling processes, finishing temperature and final cooling temperature, which exists in the controlled cooling processes was constituted by GA, and mechanical properties calculated by the optimized temperature schedule was in good accordance with desire mechanical properties. Thus, theoretically, quantitatively calculating method for flexible rolling technology was established, realizing that products with different mechanical properties were manufactured by ingot with identical chemical compositions, and that products with identical mechanical properties were manufactured by ingot with different chemical compositions.
(3) Models of the quantitative influence of roll gangs on temperature distribution were established, and method for improving the simulation precision of simulated temperature distribution was put forward. Considering that hot strip transfers heat to roll gangs, heat conductivity models for contact between roll gangs and hot strip was established. It is shown that during the whole rolling process, strip temperature dropping of about 14～18℃, and temperature at top surface of strip 4～16℃higher than that at bottom surface was affected by roll gangs, which is relatively little. So, these models can further contribute to improve the precision of simulated temperature distribution. Temperature distribution of hot strip was optimized within a narrow range by Monte Carlo algorithm. It is shown that, optimized temperature distribution was much closer to measured temperature distribution, and with this method, precision of the simulated temperature distribution can be improved, which helps to realize inline precise simulation of temperature distribution.
(4) Microstructure evolution models of pipeline X65 during recrystallization process and mean flow stress (MFS) models were established. Moreover, technology of grain refinement was put forward. Ferrite grain refinement technology was put forward and corresponding microstructure evolution was simulated after simulating recrystallization process of pipeline and analyzing evolution of austenite grain size (AGS) and recrystallization fraction at each position of 2-D cross-section which vertical to the rolling direction, It is shown that increasing reduction at the frontal finishing mill and decreasing the inlet and exit finishing temperature contributed to austenite grain refinement before transformation and transformed ferrite grain refinement in the following cooling process. It's not good either for austenite grains refinement or for protection of manufacturing device if the inlet finishing temperature was too low. However, lots of ferrite nucleation site were supplied due to the occurrence of retained strain, and ferrite grain size still can be refined. Regressed models and ANN models of mean flow stress which was more precise than Misaka models was established.
(5) Ferrite transformation models considering the effect of solute-drag effect,retained stress and AGS, etc, was created. Ferrite transformation process varying with temperature and time was determined by metallographic analysis method combined with thermal dilation curve obtained by corresponding thermal dilation experiment. And quantitative relationship between Nb content and ferrite start temperature was calculated with ferrite nucleation and growth models. It is shown that, limiting carbon concentration at the vicinity of ferrite nuclei at the moment of ferrite transformation didn't vary with temperature and cooling rate, but increased linearly with increase of Nb content in steels. Ferrite nucleation finish temperature decreased with increase of Nb content when Nb content was low, but it did not decrease when Nb content is above 0.023mass%. Solute Nb in austenite inhibited ferrite transformation. Ferrite start temperature decreased remarkably with increase of cooling rate.if cooling rate is small (<5℃·s-1), while decrease of ferrite start temperature in (direct) proportion to the increase of cooling rate if cooling rate beyond 5℃·s-1. Ferrite start temperature calculated by models agreed with the measured one. With the increasing of Nb content and Nb content beyond given values, the amount of retained stress and Nb-containing precipitations may be increased during the hot rolling process, which accelerated ferrite nucleation, improved ferrite nucleation rate and transformed volume fraction, but the increament of transformed ferrite volume fraction was small.
(6) Prediction of microstructure & properties based on physic-metallurgy models was applied to hot rolling process of plates. According to datum in the whole production process, temperature distribution, recrystallization and phase transformation evolution of plates was simulated. It is shown that predicted final microstructure agreed well with practical metallurgical structure and models of microstructure evolution for microalloyed steels was precise to predict the hot rolling process. When rolling lay in the non-recrystallied region during the 2nd rolling processing, it was advantageous to increase ferrite nucleation rate if total reduction increases. When cooling rate is small at post ferrite growth stage, increasing the cooling rate at ferrite nucleation and earlier growth stage helps to increase total ferrite nucleation ratio, refine ferrite grain size, and improve mechanical properties of steels. According to the simulated result of hot rolled plates, proper adjustment of production technology contributes to get desire mechanical properties and to realize flexible rolling technology.
(7) Main function of the developed software for flexible rolling technology setup based on prediction of microstructure & properties is as follows:prediction of microstructure evolution in hot rolling process, online prediction of mechanical properties, and inverse constitution of temperature schedule.
(1)从宏观角度定量分析了各因素与热轧板材组织性能的关系以及建立了它们之间的函数关系。以均化处理方法分析各因素对热轧板材力学性能的影响,并建立了普碳钢和微合金钢力学性能的回归模型和人工神经网络模型。结果表明,回归模型和人工神经网络模型均可以精确预测热轧产品的力学性能,且后者比前者精度高,回归模型可以为化学成分设计提供理论指导。同时,通过人工神经网络模型预测了各因素对热轧板材力学性能的影响,预测结果与均化处理结果基本一致。
(2)提出提高神经网络模型预测能力的方法以及在高精度人工神经网络模型的基础上逆向计算热轧温度制度。隐藏层单元数和训练时间等参数是影响人工神经网络模型预测能力的重要因素,提出通过遗传算法来优化神经网络结构的方法,提高了神经网络模型的预测能力。在优化后的高精度神经网络模型的基础上,通过遗传算法计算温度制度,包括第一阶段开轧温度、第二阶段开轧温度(存在于控制轧制过程中)、终轧温度和终冷温度(存在于控制冷却过程中),计算的温度制度通过回归模型计算得到的力学性能与预期的力学性能非常吻合。因此,从理论上获得了柔性化轧制技术的定量计算方法,实现了通过相同化学成分的钢坯生产不同力学性能的产品,以及通过不同化学成分的钢坯生产相同力学性能的产品。
(3)建立了输送辊道对轧件温度场定量影响模型以及提出了提高温度场模拟精度的方法。建立了输送辊与轧件之间接触的热传导模型,考虑了轧件与输送辊之间的传热。结果表明,输送辊对轧件温度场的影响相对来说比较小,大约能使轧件整体温度下降14～18℃,轧件上表面比下表面高约4-16℃,本模型有利于进一步提高热轧温度场模拟精度。通过Monte Carlo算法小幅度优化了热轧过程温度场。结果表明,优化后的温度场与实测温度更接近,这种方法也有利于提高温度场的计算精度,同时可以实现温度场的在线精确模拟。
(4)建立了管线钢X65再结晶过程微观组织演变模型和平均流变应力模型,提出了晶粒细化工艺。对管线钢再结晶过程进行模拟,分析了垂直于轧制方向的二维断面中各位置奥氏体晶粒尺寸和再结晶分数的变化过程,提出了有利于铁素体晶粒细化的工艺,并模拟了相应微观组织演变过程。结果表明,通过加大精轧入口前两个道次的压下量或者降低精轧入口温度和精轧出口温度,可以细化相变前奥氏体晶粒尺寸,有利于随后冷却过程的铁素体晶粒细化。如果精轧入口温度过低,不利于相变前奥氏体晶粒的细化和生产设备的保护,不过出现的残余应变能提供大量的铁素体形核位置,仍能获得细小的铁素体晶粒。建立了平均流变应力的回归模型和人工神经网络模型,比传统的Misaka模型更精确。
(5)建立了考虑溶质拖曳、残余应变、奥氏体晶粒等因素影响的铁素体相变模型。通过热膨胀实验获得热膨胀曲线,结合金相法确定铁素体相变随温度和时间变化的过程,在铁素体形核长大模型的基础上,计算了Nb含量与铁素体相变开始温度的定量关系。结果表明,铁素体相变开始发生时铁素体晶核周围的临界碳浓度与温度和冷却速率无关,随着钢中Nb含量的增加而线性增大；当Nb含量较低时,铁素体形核结束温度随着Nb含量的增加而降低,Nb含量大于0.023mass%后,形核温度并不会随着Nb含量的增加而降低；在奥氏体中固溶的Nb会抑制铁素体实际相变的发生；小冷却速率(<5℃·s-1)的条件下,随着冷却速率的增大,铁素体相变开始温度急剧降低,当冷却速率大于5℃·s-1时,增大的冷却速率与降低的铁素体相变开始温度近似成正比；通过模型计算的铁素体实际相变温度与实测的铁素体实际相变温度吻合；当Nb含量超过一定程度,随着微量合金元素Nb的增加,可能在热轧过程中导致残余应变的增加,以及含Nb析出物的增加,它们均能促进铁素体形核,提高铁素体形核速率,从而可能提高铁素体的转变体积分数,但是这种铁素体分数的增量很小。
(6)基于物理冶金模型的组织性能预测在板材生产过程中的应用：根据现场的所有过程数据,模拟了板材生产的温度场、再结晶过程和相变过程的演变。结果表明,预测的最终微观组织与金相组织吻合较好,微合金钢热轧微观组织演变模型具有较高预测精度；当第二阶段轧制过程位于未再结晶区时,适当增加该过程的总压下量,有利于提高随后相变过程的形核率。当铁素体后期长大阶段的冷却速率较小时,提高铁素体形核阶段和前期长大阶段的冷却速率,有利于提高总形核率,细化铁素体晶粒尺寸,提高板材的力学性能；根据热轧板材模拟结果,可以适当调整生产工艺制度,获得理想的力学性能,实现板材的柔性化轧制。
(7)开发的基于组织性能预测的柔性化轧制工艺制定软件主要功能是预测板材生产过程的微观组织演变,在线预测板材力学性能以及逆向计算温度制度。
There was a contradiction occurring with development of science, technology and economy between mass demand of iron & steel product and manufacturing technique & systemic supervisor. Therefore, it is necessary to offer a new rolling technique with larger flexibility and compatibility in rolling process, called flexible rolling technology. In other words, flexible rolling technology involves that products with distinct properties level are manufactured with raw materials with identical chemical compositions, or that products with identical properties level are manufactured with raw materials with distinct chemical compositions. Successful development of super steels, multiphase steels and corresponding devices is the practical base for the realization of flexible rolling technology, and additionally, precise prediction of microstructure and properties of hot rolled production is the academic base for that. Mass data in practical hot rolling production were collected, and relationship between each factor and microstructure & properties was systemically analyzed. In addition, Temperature models, recrystallization models and phase transformation models in hot rolling was established to exactly describe microstructure evolution of the whole hot rolling process and the foundation and mechanism of mechanical properties changing was explored. From macroscopic and microcosmic point of view, the final mechanical properties of hot rolled product were predicted by prediction technology of microstructure & properties based on Artificial Neural Network (ANN) or physic-metallurgy models. On the basis of high-precision predicting ability, temperature schedule was constituted reversely, and relationship between each factor and microstructure & properties was analyzed, as well as that production of desire strength obtained by changing temperature schedule of hot rolling was realized. In addition, effect of hot rolling parameters on corresponding microstructure evolution was analyzed, and processing system was adjusted according to mechanism of mechanical properties changing so as to realize flexible rolling of steels. According to methodology of establishing flexible technology based on prediction of microstructure and properties, corresponding software was programmed. The innovation and main research content of this paper is as follows:
(1) From macroscopic point of view, the function relationship between each factor and microstructure & properties was analyzed and created. The effect of each factor on mechanical properties was analyzed by meanly handling method, and regressed models and ANN models for mechanical properties of plain carbon steels and microalloyed steels were created. It is shown that both regressed models and ANN models can be used to precisely predict the mechanical properties of hot rolled product, and regressed models was taken as an academic guide for chemical compositions design, whereas the later model is more precise than the former one. Predicted result for the influence of each factor on the mechanical properties by ANN models is in good agreement with calculated value by meanly handling method.
(2) Method for improving predicting ability of ANN models and constituting temperature schedule reversely based on high-precision ANN models was put forward. Considering that parameters such as neurons of hidden layer and training time, etc are important factors for ANN models, an optimizing ANN architecture method by Genetic Algorithm (GA) was put forward, so that predicting ability of ANN models was improved. Based on the optimized high-precision ANN models, temperature schedule including 1st starting temperature,2nd starting temperature, which exists in the controlled rolling processes, finishing temperature and final cooling temperature, which exists in the controlled cooling processes was constituted by GA, and mechanical properties calculated by the optimized temperature schedule was in good accordance with desire mechanical properties. Thus, theoretically, quantitatively calculating method for flexible rolling technology was established, realizing that products with different mechanical properties were manufactured by ingot with identical chemical compositions, and that products with identical mechanical properties were manufactured by ingot with different chemical compositions.
(3) Models of the quantitative influence of roll gangs on temperature distribution were established, and method for improving the simulation precision of simulated temperature distribution was put forward. Considering that hot strip transfers heat to roll gangs, heat conductivity models for contact between roll gangs and hot strip was established. It is shown that during the whole rolling process, strip temperature dropping of about 14～18℃, and temperature at top surface of strip 4～16℃higher than that at bottom surface was affected by roll gangs, which is relatively little. So, these models can further contribute to improve the precision of simulated temperature distribution. Temperature distribution of hot strip was optimized within a narrow range by Monte Carlo algorithm. It is shown that, optimized temperature distribution was much closer to measured temperature distribution, and with this method, precision of the simulated temperature distribution can be improved, which helps to realize inline precise simulation of temperature distribution.
(4) Microstructure evolution models of pipeline X65 during recrystallization process and mean flow stress (MFS) models were established. Moreover, technology of grain refinement was put forward. Ferrite grain refinement technology was put forward and corresponding microstructure evolution was simulated after simulating recrystallization process of pipeline and analyzing evolution of austenite grain size (AGS) and recrystallization fraction at each position of 2-D cross-section which vertical to the rolling direction, It is shown that increasing reduction at the frontal finishing mill and decreasing the inlet and exit finishing temperature contributed to austenite grain refinement before transformation and transformed ferrite grain refinement in the following cooling process. It's not good either for austenite grains refinement or for protection of manufacturing device if the inlet finishing temperature was too low. However, lots of ferrite nucleation site were supplied due to the occurrence of retained strain, and ferrite grain size still can be refined. Regressed models and ANN models of mean flow stress which was more precise than Misaka models was established.
(5) Ferrite transformation models considering the effect of solute-drag effect,retained stress and AGS, etc, was created. Ferrite transformation process varying with temperature and time was determined by metallographic analysis method combined with thermal dilation curve obtained by corresponding thermal dilation experiment. And quantitative relationship between Nb content and ferrite start temperature was calculated with ferrite nucleation and growth models. It is shown that, limiting carbon concentration at the vicinity of ferrite nuclei at the moment of ferrite transformation didn't vary with temperature and cooling rate, but increased linearly with increase of Nb content in steels. Ferrite nucleation finish temperature decreased with increase of Nb content when Nb content was low, but it did not decrease when Nb content is above 0.023mass%. Solute Nb in austenite inhibited ferrite transformation. Ferrite start temperature decreased remarkably with increase of cooling rate.if cooling rate is small (<5℃·s-1), while decrease of ferrite start temperature in (direct) proportion to the increase of cooling rate if cooling rate beyond 5℃·s-1. Ferrite start temperature calculated by models agreed with the measured one. With the increasing of Nb content and Nb content beyond given values, the amount of retained stress and Nb-containing precipitations may be increased during the hot rolling process, which accelerated ferrite nucleation, improved ferrite nucleation rate and transformed volume fraction, but the increament of transformed ferrite volume fraction was small.
(6) Prediction of microstructure & properties based on physic-metallurgy models was applied to hot rolling process of plates. According to datum in the whole production process, temperature distribution, recrystallization and phase transformation evolution of plates was simulated. It is shown that predicted final microstructure agreed well with practical metallurgical structure and models of microstructure evolution for microalloyed steels was precise to predict the hot rolling process. When rolling lay in the non-recrystallied region during the 2nd rolling processing, it was advantageous to increase ferrite nucleation rate if total reduction increases. When cooling rate is small at post ferrite growth stage, increasing the cooling rate at ferrite nucleation and earlier growth stage helps to increase total ferrite nucleation ratio, refine ferrite grain size, and improve mechanical properties of steels. According to the simulated result of hot rolled plates, proper adjustment of production technology contributes to get desire mechanical properties and to realize flexible rolling technology.
(7) Main function of the developed software for flexible rolling technology setup based on prediction of microstructure & properties is as follows:prediction of microstructure evolution in hot rolling process, online prediction of mechanical properties, and inverse constitution of temperature schedule.
引文
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